SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach
- URL: http://arxiv.org/abs/2411.11195v2
- Date: Tue, 19 Nov 2024 11:22:23 GMT
- Title: SoK: Unifying Cybersecurity and Cybersafety of Multimodal Foundation Models with an Information Theory Approach
- Authors: Ruoxi Sun, Jiamin Chang, Hammond Pearce, Chaowei Xiao, Bo Li, Qi Wu, Surya Nepal, Minhui Xue,
- Abstract summary: Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence.
This paper conceptualizes cybersafety and cybersecurity in the context of multimodal learning.
We present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats.
- Score: 58.93030774141753
- License:
- Abstract: Multimodal foundation models (MFMs) represent a significant advancement in artificial intelligence, combining diverse data modalities to enhance learning and understanding across a wide range of applications. However, this integration also brings unique safety and security challenges. In this paper, we conceptualize cybersafety and cybersecurity in the context of multimodal learning and present a comprehensive Systematization of Knowledge (SoK) to unify these concepts in MFMs, identifying key threats to these models. We propose a taxonomy framework grounded in information theory, evaluating and categorizing threats through the concepts of channel capacity, signal, noise, and bandwidth. This approach provides a novel framework that unifies model safety and system security in MFMs, offering a more comprehensive and actionable understanding of the risks involved. We used this to explore existing defense mechanisms, and identified gaps in current research - particularly, a lack of protection for alignment between modalities and a need for more systematic defense methods. Our work contributes to a deeper understanding of the security and safety landscape in MFMs, providing researchers and practitioners with valuable insights for improving the robustness and reliability of these models.
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